Please use this identifier to cite or link to this item:
Title: A Theory for Dynamic Weighting in Monte Carlo Computation
Authors: Liu, J.S.
Liang, F. 
Wong, W.H.
Keywords: Gibbs sampling
Importance sampling
Ising model
Metropolis algorithm
Neural network
Renewal theory
Simulated annealing
Simulated tempering
Issue Date: Jun-2001
Source: Liu, J.S.,Liang, F.,Wong, W.H. (2001-06). A Theory for Dynamic Weighting in Monte Carlo Computation. Journal of the American Statistical Association 96 (454) : 561-573. ScholarBank@NUS Repository.
Abstract: This article provides a first theoretical analysis of a new Monte Carlo approach, the dynamic weighting algorithm, proposed recently by Wong and Liang. In dynamic weighting Monte Carlo, one augments the original state space of interest by a weighting factor, which allows the resulting Markov chain to move more freely and to escape from local modes. It uses a new invariance principle to guide the construction of transition rules. We analyze the behavior of the weights resulting from such a process and provide detailed recommendations on how to use these weights properly. Our recommendations are supported by a renewal theory-type analysis. Our theoretical investigations are further demonstrated by a simulation study and applications in neural network training and Ising model simulations.
Source Title: Journal of the American Statistical Association
ISSN: 01621459
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Mar 9, 2018

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.